Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.